grid observatory @ ccgrid 2011

The Grid Observatory Cécile Germain-Renaud , Alain Cady , Philippe Gauron ,

Michel Jouvin , Charles Loomis , Janusz Martyniak , Julien Nauroy , Guillaume Philippon , Michèle Sebag

Goals (I): Digital curation

�  For the behavioral data of the EGEE/EGI grid �  Collection,

preservation, indexation, querying

�  Continuous and exhaustive datasets

�  For scientific and engineering usage

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Goals (II): model and optimize

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Complex systems description

Statistical and Machine Learning models and optimization

Applications to dimensioning and Autonomics

Outline

� What is the GO?

� Epistemological thoughts

� How the GO helps, with scientific issues

� Ongoing work

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Outline




� Ongoing work

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Who are we?

�  Born in the flagship EU grid project EGEE

�  Presently a collaborative effort of �  CNRS/UPS Laboratoire de Recherche en Informatique

�  CNRS/UPS Laboratoire de l'Accélérateur Linéaire �  Imperial College London

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Who are we?

�  With the support of �  France Grilles – French NGI of EGI �  EGI-Inspire �  Ile de France council

(Software and Complex Systems programme) �  INRIA – Saclay (ADT programme) �  CNRS (PEPS programme) �  University Paris Sud (MRM programme)

�  Scientific Collaborations �  NSF Center for Autonomic Computing �  European Middleware Initiative �  Institut des Systèmes Complexes �  Cardiff University

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The digital data

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JOBS LIFECYCLE: SYNTHETIC JOBS LIFECYCLE: DETAILED

JOBS: TORQUE VIEW

GRID STATUS – SELF AWARENESS

MIDDLEWARE INTERNALS

FILE TRAFFIC

Architecture

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Grid Services

Grid-Observatory Scripts

Torque

WMS

CE Logging & bookkepping

BDII IC RTM

Incoming Anonymisation Upload

Storage Elements DPM via HTTPs Grid Observatory Portal

SFTP SQL LDAP HTTP •  Native data •  Often as detailed as on-line •  On top of the mainstream monitoring tools •  Consistent anonymization

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Production since October 2008

Traces available through the portal: no grid certificate required

www.grid-observatory.org

Portal Usage

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Use and users both increasing steadily

Global Impact

Outline




� Ongoing work

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Why digital curation?

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�  How much of your research (and mine) went to the real world?

�  We need to show that the research has verifiable and positive impact on production systems

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�  How much of your research (and mine) went to the real world?

�  We need to show that the research has verifiable and positive impact on production systems « beyond any reasonable doubt »

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How we configure our grids? Courtesy James Casey talk @EGEE09

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The MAPE-K loop

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Managed Element E S

Monitor

Analyze

Execute

Plan

Knowledge

Autonomic Manager E S

The MAPE-K loop

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Managed Element E S

Monitor

Analyze

Execute

Plan

Knowledge

Autonomic Manager E S

State-Space and Data Abstraction Streaming: On-line data mining, clustering,.. Dimensionality reduction Active learning Ontological inference

High-dimensional, high-volume ‘raw’ data

Compressed, ‘informative’ data

The acquisition/analysis feedback loop

�  Analysis informs acquisition: a priori feature definition may be seriously misleading.

�  Example of �  A priori redundant features

�  Quasi-linear complexity data streaming demonstrated

�  Also : on the sampling frequency of acquisition for the IS, eg [Laurence Field and Rizos Sakellariou. How Dynamic is the Grid? Towards a Quality Metric for Grid Information Systems. Grid’2010]

�  ...

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Outline




� Ongoing work

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Issue I: Uncertainty (1/2)

�  As a dynamic(al) system �  Entities change behavior as an effect of unexpected

feedbacks, emergent behavior �  Organized self-criticality, minority games,...

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Some scientific issues

�  Uncertainty �  As a dynamic(al) system

�  Entities change behavior as an effect of unexpected feedbacks, emergent behavior

�  Organized self-criticality, minority games,...

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Complexity???

Symbolic Dynamics for Discrete Adaptive Games

Cosma Rohilla Shalizi, David J. Albers

http://www.santafe.edu/media/workingpapers/02-07-031.pdf

We use symbolic dynamics to study discrete adaptive games, such as the minority game and the El Farol Bar problem. We show that no such game can have deterministic chaos. We put upper bounds on the statistical complexity and period of these games; the former is at most linear in the number agents and the size of their memories. We extend our results to cases where the players have infinite-duration memory (they are still non-chaotic) and to cases where there is ``noise'' in the play (leaving the complexity unchanged or even reduced). We conclude with a mechanism that can reconcile our findings with the phenomenology, and reflections on the merits of simple models of mutual adaptation.

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Issue I: Uncertainty (2/2)

�  As a dynamic(al) system �  Entities change behavior as an effect of unexpected

feedbacks, emergent behavior �  Organized self-criticality, minority games,...

�  Lack of complete and common knowledge – Information uncertainty �  Monitoring is distributed too

�  Resolution and calibration �  Semantics and ontologies

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Resolution and calibration

Semantics and ontologies

Issue II: Fundamentals in statistics

� Statistical significance

� Is prediction possible?

� Which metrics (mathematical sense)?

� And more

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Statistical significance

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Extreme values may dominate the statistics

Ò  Can we predict anything? É  Maybe, but difficult: same as

earthquakes and finance

Metrics

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Root Mean Squared Error is inadequate

Metrics

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Root Mean Squared Error is inadequate

The ROC metric: à la BQP

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Fundamentals in statistics

� Statistical significance

� Is prediction possible?

� Which metrics (mathematical sense)?

� Are our systems stationary?

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Descriptive and generative models

�  The “physical” process is not stationary �  Trends: Rogers’s curve

�  Technology innovations

�  Real-world events

�  Experimental discoveries

�  Slashdotted accesses

�  Non-stationarity and heavy-tailedness can easily be confused

�  Non-stationarity is a reasonable alternative

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Dealing with non-stationarity

�  Statistical testing: jump in… �  Theoretical guarantees for

known distributions

�  Segmentation �  AIC, MDL,… – based �  Mostly off-line and

computationally expensive �  A-priori hypotheses on the

segment models

�  Adaptive clustering �  The exemplars are the model �  On-line rupture detection:

back to statistical testing, but on the indicators, not on the model

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Outline




� Ongoing work

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Lessons learned

�  Sociology �  Running a production system for usage by computer

science is nearly unchartered territory – we are a few explorators

�  Verified that 80% of the cost of Data Mining is in pre-processing

�  Technique �  Build on existing monitoring tools

�  No fancy technology: the goal is usage, not the tool

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Ongoing work

�  Energy monitoring �  Unique facility reporting detailed data at the

motherboard level �  Method and roadmap to be annouced at the

GreenDays next week

�  Grid Observatory v2.0: "services make the repository" �  Semantic data organization �  On-line visualization

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Ongoing work

�  Energy monitoring �  Unique facility reporting detailed data at the motherboard

level

�  Method and roadmap to be annouced at the GreenDays next week

�  Grid Observatory v2.0: "services make the repository" �  Semantic data organization

�  On-line visualization

�  Keep-on with monitoring standardisation effort at EMI

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More information

�  Coll. with Autonomic Computing �  S. Jha’s talk http://www.youtube.com/watch?v=DI62pG_HBcs

�  GMAC Panel published by IEEE Internet Computing and www.computer.org/portal/web/computingnow/panel

�  Autonomics research �  Adaptive clustering with application to fault diagnosis: “Toward Autonomic

Grids: Analyzing the Job Flow with Affinity Streaming”, SIGKDD'2009 �  MDL segmentation applied to workload: “Discovering Piecewise Linear

Models of Grid Workload”, CCGRID 2010 �  Fault models: “Optimization of jobs submission on the EGEE production

grid: modeling faults using workload”. Journal of Grid Computing 8(2) �  Cloud management: Energy-efficient application-aware online provisioning

for virtualized clouds and data centers. International Conference on Green Computing 2010

�  And much more on the GO portal, Documents section

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www.grid-observatory.org